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The learning outcomes are to remember the overall context of global supply chain management from a stakeholder perspective, to understand the context of material handling movement in a mining industry, to apply the overall knowledge of linear programming in a supply chain context, to analyze the different constraints with flow of goods at different nodes in various location hubs and convert the same into the optimization problem and to evaluate carefully the different costs associated at different levels and then finding the optimal solution that minimizes the total cost.

This case proposes a mixed integer multi-echelon analytical model integrated with the scenario tree analysis. The integrated model is used to optimize the allocation of volumes at various stages of the supply chain of exporters of bulk materials like iron ore from Goa, India, to various countries in Asia. The scenario tree analysis is then used to evaluate decisions under certainty with demand as the stochastic parameter. The proposed integrated model has potential for collaboration in the supply chain and facilitating network design, inventory and transportation planning and policy analysis.

This course is suitable at the MBA level for the following courses: Operations Research (Focus/Session: Applications on Supply Chain Management), Supply Chain Management (Focus/Session: Global Supply Chain Management, Logistics Planning, Distribution Network), Logistics Management (Focus/Session: Transportation Planning) nd Operations Strategy (Focus/Session: Location Node Strategy).

Teaching Notes are available for educators only.

CSS 9: Operations and Logistics.

This case is designed to enable students to understand the role of women in artificial intelligence (AI); understand the importance of ethics and diversity in the AI field; discuss the ethical issues of AI; study the implications of unethical AI; examine the dark side of corporate-backed AI research and the difficult relationship between corporate interests and AI ethics research; understand the role played by Gebru in promoting diversity and ethics in AI; and explore how Gebru can attract more women researchers in AI and lead the movement toward inclusive and equitable technology.

The case discusses how Timnit Gebru (She), a prominent AI researcher and former co-lead of the Ethical AI research team at Google, is leading the way in promoting diversity, inclusion and ethics in AI. Gebru, one of the most high-profile black women researchers, is an influential voice in the emerging field of ethical AI, which identifies issues based on bias, fairness, and responsibility. Gebru was fired from Google in December 2020 after the company asked her to retract a research paper she had co-authored about the pitfalls of large language models and embedded racial and gender bias in AI. While Google maintained that Gebru had resigned, she said she had been fired from her job after she had raised issues of discrimination in the workplace and drawn attention to bias in AI. In early December 2021, a year after being ousted from Google, Gebru launched an independent community-driven AI research organization called Distributed Artificial Intelligence Research (DAIR) to develop ethical AI, counter the influence of Big Tech in research and development of AI and increase the presence and inclusion of black researchers in the field of AI. The case discusses Gebru’s journey in creating DAIR, the goals of the organization and some of the challenges she could face along the way. As Gebru seeks to increase diversity in the field of AI and reduce the negative impacts of bias in the training data used in AI models, the challenges before her would be to develop a sustainable revenue model for DAIR, influence AI policies and practices inside Big Tech companies from the outside, inspire and encourage more women to enter the AI field and build a decentralized base of AI expertise.

This case is meant for MBA students.

Teaching Notes are available for educators only.

CCS 11: Strategy

In late 2019, Dr. Hiroaki Kitano, the president and director of research at Sony Computer Science Laboratories (Sony CSL), had decided he would be stepping down from his position soon. Sony CSL, a small blue-sky fundamental research facility funded by Sony, had always operated on the strength of the trust between Sony's CEO and the lab's director. Sony had been hands-off in its management, leaving Kitano to hire, fire, fund, and evaluate the lab's researchers and project portfolio at his own discretion. Now that he was stepping down, however, he worried that Sony CSL could not withstand his departure. Kitano wanted to make a transparent plan for the organization's future before he handed off Sony CSL to his successor. That plan involved three key decisions. First, what should be the optimal structure and governance of Sony CSL? Should it maintain its independence and autonomy, or should it align more closely with Sony's business priorities? Second, how could Sony CSL scale its impact on Sony and society at large, given its small size? Finally, should Sony CSL establish some standard methods of measuring project success and strength of the portfolio? In making these decisions, Kitano wanted to ensure that he preserved the unique culture that had allowed Sony CSL to pursue path-breaking research and innovation.